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A Framework for Developing the Structure of Public Health Economic Models

Overview of attention for article published in Value in Health (Elsevier Science), July 2016
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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4 tweeters
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1 Facebook page

Citations

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11 Dimensions

Readers on

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104 Mendeley
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Title
A Framework for Developing the Structure of Public Health Economic Models
Published in
Value in Health (Elsevier Science), July 2016
DOI 10.1016/j.jval.2016.02.011
Pubmed ID
Authors

Hazel Squires, James Chilcott, Ronald Akehurst, Jennifer Burr, Michael P. Kelly

Abstract

A conceptual modeling framework is a methodology that assists modelers through the process of developing a model structure. Public health interventions tend to operate in dynamically complex systems. Modeling public health interventions requires broader considerations than clinical ones. Inappropriately simple models may lead to poor validity and credibility, resulting in suboptimal allocation of resources. This article presents the first conceptual modeling framework for public health economic evaluation. The framework presented here was informed by literature reviews of the key challenges in public health economic modeling and existing conceptual modeling frameworks; qualitative research to understand the experiences of modelers when developing public health economic models; and piloting a draft version of the framework. The conceptual modeling framework comprises four key principles of good practice and a proposed methodology. The key principles are that 1) a systems approach to modeling should be taken; 2) a documented understanding of the problem is imperative before and alongside developing and justifying the model structure; 3) strong communication with stakeholders and members of the team throughout model development is essential; and 4) a systematic consideration of the determinants of health is central to identifying the key impacts of public health interventions. The methodology consists of four phases: phase A, aligning the framework with the decision-making process; phase B, identifying relevant stakeholders; phase C, understanding the problem; and phase D, developing and justifying the model structure. Key areas for further research involve evaluation of the framework in diverse case studies and the development of methods for modeling individual and social behavior. This approach could improve the quality of Public Health economic models, supporting efficient allocation of scarce resources.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 <1%
United States 1 <1%
Unknown 102 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 21%
Student > Master 19 18%
Researcher 17 16%
Unspecified 10 10%
Other 9 9%
Other 27 26%
Readers by discipline Count As %
Medicine and Dentistry 27 26%
Unspecified 20 19%
Economics, Econometrics and Finance 16 15%
Social Sciences 9 9%
Nursing and Health Professions 8 8%
Other 24 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 April 2016.
All research outputs
#6,843,294
of 13,221,044 outputs
Outputs from Value in Health (Elsevier Science)
#1,022
of 2,491 outputs
Outputs of similar age
#99,532
of 263,733 outputs
Outputs of similar age from Value in Health (Elsevier Science)
#30
of 94 outputs
Altmetric has tracked 13,221,044 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,491 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,733 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.